The posting
The Team
You will join a dynamic AI Infrastructure team focused on enabling high-performance AI across Zoom's products and services. The team builds the core systems that support model training, deployment, and inference at scale, driving innovation in areas such as real-time communication, computer vision, and natural language understanding.
What you can expect
You will build and optimize the inference systems that serve Zoom's AI models in production. Working alongside senior inference engineers, research and platform teams, you will profile serving workloads, find the bottlenecks that limit latency, throughput and cost, and ship the fixes. You will work close to the metal: inside inference frameworks, GPU profiles and serving infrastructure.
Responsibilities
- Build, tune and maintain components of Zoom's inference serving stack for transformer and multimodal models, including large and Mixture-of-Experts (MoE) architectures
- Implement and evaluate inference optimisations such as speculative decoding, continuous batching, KV cache management, parallelism layouts and quantisation (INT4/INT8/FP8)
- Extend and customise inference frameworks (vLLM, SGLang, TensorRT-LLM or equivalent) to support new models and Zoom's production requirements
- Profile GPU workloads with Nsight Systems or equivalent tools, root-cause performance regressions, and validate improvements with reproducible benchmarks
- Build and improve internal tooling for benchmarking, performance modelling and serving configuration
- Partner with researchers to bring new model architectures into production efficiently
- Contribute to on-call and production support for the services you build, with guidance from senior engineers
What we're looking for
- A degree in Computer Science, Computer Engineering or a related field (MS or PhD preferred), or equivalent practical experience
- 0–3 years of software engineering experience; internships, research and open-source work in ML systems count
- Hands-on experience with at least one inference framework (vLLM, SGLang, TensorRT-LLM or ONNX Runtime), at the level of serving and modifying it, not only exporting models
- Working knowledge of how LLM inference performs on GPUs: memory bandwidth, KV cache, batching, and communication across GPUs
- Experience profiling and debugging GPU performance, with evidence of an optimisation you measured and shipped
- Depth in at least one of: speculative decoding, continuous batching, KV cache design, quantisation, distributed or multi-GPU serving
- Strong programming skills in Python and C++, and comfort reading and changing large framework codebases
Preferred
- Merged contributions to vLLM, SGLang, TensorRT-LLM, PyTorch or similar projects
- CUDA, Triton or CUTLASS kernel development
- Experience serving MoE or 100B+ parameter models, or multi-node inference (NCCL, RDMA/RoCE)
- Graduate coursework or research in ML systems, compilers or high-performance computing
Salary Range or On Target Earnings:
$137,700.00 - $275,400.00
In addition to the base salary and/or OTE listed Zoom has a Total Direct Compensation philosophy that takes into consideration; base salary, bonus and equity value.
Note: Starting pay will be based on a number of factors and commensurate with qualifications & experience.
We also have a location based compensation structure; there may be a different range for candidates in this and other locations
At Zoom, we offer a window of at least 5 days for you to apply because we believe in giving you every opportunity. Below is the potential closing date, just in case you want to mark it on your calendar. We look forward to receiving your application!
Anticipated Position Close Date:
11/13/26
Ways of Working Our structured hybrid approach is centered around our offices and remote work environments. The work style of each role, Hybrid, Remote, or In-Person is indicated in the job description/posting.
Benefits As part of our award-winning workplace culture and commitment to delivering happiness, our benefits program offers a variety of perks, benefits, and options to help employees maintain their physical, mental, emotional, and financial health; support work-life balance; and contribute to their community in meaningful ways. Click Learn for more information.
About Us Zoomies help people stay connected so they can get more done together. We set out to build the best collaboration platform for the enterprise, and today help people communicate better with products like Zoom Contact Center, Zoom Phone, Zoom Events, Zoom Apps, Zoom Rooms, and Zoom Webinars. We’re problem-solvers, working at a fast pace to design solutions with our customers and users in mind. Find room to grow with opportunities to stretch your skills and advance your career in a collaborative, growth-focused environment.
Our Commitment
At Zoom, we believe great work happens when people feel supported and empowered. We’re committed to fair hiring practices that ensure every candidate is evaluated based on skills, experience, and potential. If you require an accommodation during the hiring process, let us know—we’re here to support you at every step.
If you need assistance navigating the interview process due to a medical disability, please submit an Accommodations Request Form and someone from our team will reach out soon. This form is solely for applicants who require an accommodation due to a qualifying medical disability. Non-accommodation-related requests, such as application follow-ups or technical issues, will not be addressed.
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